A Fever Dream of Machine Learning Framework Composability
7In machine learning (ML), we've long dreamed of - and spearheaded - seamless interoperability between frameworks, datasets, and tools to build big and capable ML systems. Much progress has been made towards driving down the transaction costs of ML, in turn making more ML tasks feasible. In this talk I will take a look at different points in the ML systems stack, from interoperable data formats over data generation all the way to the community norms around it, to touch on successes and challenges towards the composable ML systems fever dream. I'll explore how composable infrastructure can enable new possibilities for high-impact domains such as healthcare and geospatial, where cross-pollination between data-centric methods and domain expertise is critical. Drawing from our experiences building data-centric ML communities and infrastructure, the talk will conclude with a vision for the future where practitioners get the most cost-efficient utility out of their data without a fever.
Speaker: Luis Oala, Dotphoton
Learn more about Microsoft Research Lab – Africa, Nairobi: https://www.microsoft.com/en-us/research/lab/microsoft-research-lab-africa-nairobi/seminars/